Induction of Functional Logic Programs

José Hernández-Orallo, M. José Ramírez-Quintana

Abstract

A framework for the Induction of Functional Logic Programs (IFLP) from facts is presented. Inspired in the inverse resolution operator of ILP, we study the reversal of narrowing, the most usual operational mechanism for functional logic programming. We also generalize the selection criteria for guiding the search, including coherence criteria in addition to the MDL principle. A non-incremental learning algorithm and a more sophisticated incremental extension of it are presented. We discuss the advantages of IFLP over ILP, most of which are inherited from the power of narrowing w.r.t. resolution and the limitation of conditions, a usual gate for extensional exceptions. At the end of this paper, we comment on the adaptability of our techniques to higher-order induction.

Keywords: Functional Logic Programming, Inductive Logic Programming, Machine Learning.